| Issue |
E3S Web Conf.
Volume 200, 2020
The 1st Geosciences and Environmental Sciences Symposium (ICST 2020)
|
|
|---|---|---|
| Article Number | 02016 | |
| Number of page(s) | 5 | |
| Section | Environmental Management | |
| DOI | https://doi.org/10.1051/e3sconf/202020002016 | |
| Published online | 23 October 2020 | |
Improving normalization method of higher-order neural network in the forecasting of oil production
Department of Electrical Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
* Corresponding author: joko.nugroho.p@mail.ugm.ac.id
One of the challenges in the oil industry is to predict well production in the absence of frequent flow measurement. Many researches have been done to develop production forecasting in the petroleum area. One of the machine learning approach utilizing higher-order neural network (HONN) have been introduced in the previous study. In this study, research focus on normalization impact to the HONN model, specifically for univariate time-series dataset. Normalization is key aspect in the pre-processing stage, moreover in neural network model.
Key words: oil production forecast / time-series / higher-order neural network / normalization.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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